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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.11317 |
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| _version_ | 1866918158911143936 |
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| author | Fazelnia, Mohamad Moshtari, Sara Mirakhorli, Mehdi |
| author_facet | Fazelnia, Mohamad Moshtari, Sara Mirakhorli, Mehdi |
| contents | In the rapidly evolving field of artificial intelligence (AI), the identification, documentation, and mitigation of vulnerabilities are paramount to ensuring robust and secure systems. This paper discusses the minimum elements for AI vulnerability management and the establishment of an Artificial Intelligence Vulnerability Database (AIVD). It presents standardized formats and protocols for disclosing, analyzing, cataloging, and documenting AI vulnerabilities. It discusses how such an AI incident database must extend beyond the traditional scope of vulnerabilities by focusing on the unique aspects of AI systems. Additionally, this paper highlights challenges and gaps in AI Vulnerability Management, including the need for new severity scores, weakness enumeration systems, and comprehensive mitigation strategies specifically designed to address the multifaceted nature of AI vulnerabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_11317 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Establishing Minimum Elements for Effective Vulnerability Management in AI Software Fazelnia, Mohamad Moshtari, Sara Mirakhorli, Mehdi Cryptography and Security In the rapidly evolving field of artificial intelligence (AI), the identification, documentation, and mitigation of vulnerabilities are paramount to ensuring robust and secure systems. This paper discusses the minimum elements for AI vulnerability management and the establishment of an Artificial Intelligence Vulnerability Database (AIVD). It presents standardized formats and protocols for disclosing, analyzing, cataloging, and documenting AI vulnerabilities. It discusses how such an AI incident database must extend beyond the traditional scope of vulnerabilities by focusing on the unique aspects of AI systems. Additionally, this paper highlights challenges and gaps in AI Vulnerability Management, including the need for new severity scores, weakness enumeration systems, and comprehensive mitigation strategies specifically designed to address the multifaceted nature of AI vulnerabilities. |
| title | Establishing Minimum Elements for Effective Vulnerability Management in AI Software |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2411.11317 |